Data sequences are often redundant to a high extent, i.e. they contain a basically higher number of data elements or signs than are necessary for representing the information they contain at the time. An example for this is the regularly constructed data sets (records) for files pertaining to the economy and to the administration. A further example of this is the transmission of TV pictures in which the subsequently taken and then as data sequences stored or transmitted partial images, relative to individual areas of a scene, are identical, since they were not exposed to any change relative to their position, light intensity or color of picture points (elements). This is the case for the majority of picture points without changing the scene. In order to be able to carry out the transmission of data sequences economically, it is necessary to compress them and to expand them again at the end of the transmission path (route).
Processes are already known in which the compression of data sequences is made by means of a coding of variable length. The coding diagram of this type effects that the bit strings of a fixed predetermined length are coded in bit strings of variable length, and bit strings of the often occurring code words are represented shorter than code words of a fixed predetermined length. This conversion of code words of a fixed length into code words of variable length is known as Huffman-coding. In case of a meaningful allocation of code words of variable length with the given data, a considerably slighter length of such code words will then result in comparison with the respective code words of a predetermined fixed length, though many code words of variable length are considerably longer than the corresponding code words of a fixed length.
Furthermore, processes for compressing and expanding data sequences are known in which a structural analysis is applied, in order to recognize in a predetermined number of data elements of data sequences a characteristic that connects them. Such a characteristic can be, for example, in a repeated occurrence of the same sign or can be given also in an increasing numerical character string of the type 1, 2 . . . . The given number of data elements is converted then into a code. Such a code in a string of signs equal to n should contain at least a separating sign, the respective sign and the number of its occurrence, in order to be able to produce again the original string during expansion.
Further known is a process for compressing and expanding data sequences in which two logically associated data sequences are interlinked by an EXCLUSIVE-OR before the compression proper of the usual type. This logical interlinkage (combination) effects that zeros are produced on the points on which equal signs are present. Since the data sequences are presumably logically associated by EXCLUSIVE-OR association, a data sequence is produced which often contains zeroes as data elements. The data sequence strongly interspersed with zeroes is then compressed in the usual manner.
The structural association, especially the logic association of at least two data sequences for the purpose of compressing, is used in this process in order to improve the result of compressing. However, even in this process, the result of compressing is still strongly redundant. Besides, a high technical circuitry expenditure is necessary for the realization of this process, since universal computers of high efficiency are required.
The same is true for a process of the initially mentioned type that is known from the article "Data Compaction for Improved Transmission Efficiency" by Dirk R. Klose in IEEE National Telecommunications Conference, of Nov. 26 to 28, 1973, pp. 35C-1 to 35C-6. In this process, several data sequences of a predetermined length, forming together a data set, are arranged line by line in a matrix and compressed by coding column by column into less redundant code sequences, so that their structural association is set in the direction of the columns and evaluated. Although it is possible to improve the result of compression in this manner, the expenditure of the technical circuitry, nevertheless, remains high, especially when considering the matrix-like data sequences arranged column by column.